Activity and Selectivity Trends in Synthesis Gas Conversion to Higher Alcohols

SPOCK Tool for Constructing Empirical Volcano Diagrams from Catalytic Data

Volcano plots, stemming from the Sabatier principle, visualize descriptor-performance relationships, allowing rational catalyst design. Manually drawn volcanoes originating from experimental studies are potentially prone to human bias as no guidelines or metrics exist to quantify the goodness of fit. To address this limitation, we introduce a framework called SPOCK (systematic piecewise regression for volcanic kinetics) and validate it using experimental data from heterogeneous, homogeneous, and enzymatic catalysis to fit volcano-like relationships. We then generalize this approach to DFT-derived volcanoes and evaluate the tool's robustness against noisy kinetic data and in identifying false-positive volcanoes, i.e., cases where studies claim a volcano-like relationship exists, but such correlations are not statistically significant. Once the SPOCK's functional features are established, we demonstrate its potential to identify descriptor-performance relationships, exemplified via the ceria-promoted water-gas shift and single-atom-catalyzed electrocatalytic carbon dioxide reduction reactions. In both cases, the model uncovers descriptors previously unreported, revealing insights that are not easily recognized by human experts. Finally, we showcase SPOCK's capabilities to formulate multivariable descriptors, an emerging topic in catalysis research. Our work pioneers an automated and standardized tool for volcano plot construction and validation, and we release the model as an open-source web application for greater accessibility and knowledge generation in catalysis.

Unifying thermochemistry concepts in computational heterogeneous catalysis

Thermophysical properties of adsorbates and gas-phase species define the free energy landscape of heterogeneously catalyzed processes and are pivotal for an atomistic understanding of the catalyst performance. These thermophysical properties, such as the free energy or the enthalpy, are typically derived from density functional theory (DFT) calculations. Enthalpies are species-interdependent properties that are only meaningful when referenced to other species. The widespread use of DFT has led to a proliferation of new energetic data in the literature and databases. However, there is a lack of consistency in how DFT data is referenced and how the associated enthalpies or free energies are stored and reported, leading to challenges in reproducing or utilizing the results of prior work. Additionally, DFT suffers from exchange-correlation errors that often require corrections to align the data with other global thermochemical networks, which are not always clearly documented or explained. In this review, we introduce a set of consistent terminology and definitions, review existing approaches, and unify the techniques using the framework of linear algebra. This set of terminology and tools facilitates the correction and alignment of energies between different data formats and sources, promoting the sharing and reuse of ab initio data. Standardization of thermochemistry concepts in computational heterogeneous catalysis reduces computational cost and enhances fundamental understanding of catalytic processes, which will accelerate the computational design of optimally performing catalysts.

Uncertainty Quantification of Linear Scaling, Machine Learning, and Density Functional Theory Derived Thermodynamics for the Catalytic Partial Oxidation of Methane on Rhodium

Accurate and complete microkinetic models (MKMs) are powerful for anticipating the behavior of complex chemical systems at different operating conditions. In heterogeneous catalysis, they can be further used for the rapid development and screening of new catalysts. Density functional theory (DFT) is often used to calculate the parameters used in MKMs with relatively high fidelity. However, given the high cost of DFT calculations for adsorbates in heterogeneous catalysis, linear scaling relations (LSRs) and machine learning (ML) models were developed to give rapid estimates of the parameters in MKM. Regardless of the method, few studies have attempted to quantify the uncertainty in catalytic MKMs, as the uncertainties are often orders of magnitude larger than those for gas phase models. This study explores uncertainty quantification and Bayesian Parameter Estimation for thermodynamic parameters calculated by DFT, LSRs, and GemNet-OC, a ML model developed under the Open Catalyst Project. A model for catalytic partial oxidation of methane (CPOX) on Rhodium was chosen as a case study, in which the model's thermodynamic parameters and their associated uncertainties were determined using DFT, LSR, and GemNet-OC. Markov Chain Monte Carlo coupled with Ensemble Slice Sampling was used to sample the highest probability density (HPD) region of the posterior and determine the maximum of the a posteriori (MAP) for each thermodynamic parameter included. The optimized microkinetic models for each of the three estimation methods had quite similar mechanisms and agreed well with the experimental data for gas phase mole fractions. Exploration of the HPD region of the posterior further revealed that adsorbed hydroxide and oxygen likely bind on facets other than Rhodium 111. The demonstrated workflow addresses the issue of inaccuracies arising from the integration of data from multiple sources by considering both experimental and computational uncertainties, and further reveals information about the active site that would not have been discovered without considering the posterior.

Active Sites on the CuCo Catalyst in Higher Alcohol Synthesis from Syngas: A Review

Higher alcohol synthesis through the Fischer-Tropsch (F-T) process was considered a promising route for the efficient utilization of fossil resources could be achieved. The CuCo catalysts were proven to be efficient candidates and attracted much interest. Great efforts have been made to investigate the active sites and mechanisms of CuCo catalysts. However, the industrialized application of CuCo catalysts in this process was still hindered. The poor stability of this catalyst was one of the main reasons. This short review summarized the recent development of active sites on the CuCo catalysts for higher alcohol synthesis, including CuCo alloy particles, CuCo core-shell particles, and unsaturated particles. The complex active sites and their continual changes during the reaction led to the poor stability of the catalysts. The effect of active sites on catalytic performance was discussed. Furthermore, the key factors in fabricating stable CuCo catalysts were proposed. Finally, reasonable proposals were proposed for designing efficient and stable CuCo catalysts in higher alcohol synthesis.

CO2 Hydrogenation on Ru Single-Atom Catalyst Encapsulated in Silicalite: a DFT and Microkinetic Modeling Study

The critical levels of CO2 emissions reached in the past decade have encouraged researchers into finding techniques to reduce the amount of anthropogenic CO2 expelled to the atmosphere. One possibility is to capture the produced CO2 from the source of emission or even from air (i.e., direct air capture) by porous materials (e.g., zeolites and MOFs). Among the different usages of captured CO2, its conversion into light fuels such as methane, methanol, and formic acid is essential for ensuring the long-awaited circular economy. In the last years, single-atom catalysts encapsulated in zeolites have been considered to this purpose since they exhibit a high selectivity and activity with the minimum expression of catalytic species. In this study, a detailed mechanism composed by 47 elementary reactions, 42 of them in both forward and reverse directions and 5 of them that correspond to the desorption of gas products just forwardly studied), has been proposed for catalytic CO2 hydrogenation over Ru SAC encapsulated in silicate (Ru1@S-1). Periodic density functional theory (DFT) calculations along with microkinetic modeling simulations at different temperatures and pressures were performed to evaluate the evolution of species over time. The analysis of the results shows that carbon monoxide is the main gas produced, followed by formic acid and formaldehyde. The rate analysis shows that CO(g) is formed mainly through direct dissociation of CO2 (i.e., redox mechanism), whereas COOH formation is assisted by OH. Moreover, the Campbell's degree of rate control analysis suggests that the determining steps for the formation of CO(g) and CH2O(g) gas species are their own desorption processes. The results obtained are in line with recent experimental and theoretical results showing that Ru1 SACs are highly selective to CO(g), whereas few atom clusters as Ru4 increase selectivity toward methane formation.

Active learning streamlines development of high performance catalysts for higher alcohol synthesis

Developing efficient catalysts for syngas-based higher alcohol synthesis (HAS) remains a formidable research challenge. The chain growth and CO insertion requirements demand multicomponent materials, whose complex reaction dynamics and extensive chemical space defy catalyst design norms. We present an alternative strategy by integrating active learning into experimental workflows, exemplified via the FeCoCuZr catalyst family. Our data-aided framework streamlines navigation of the extensive composition and reaction condition space in 86 experiments, offering >90% reduction in environmental footprint and costs over traditional programs. It identifies the Fe65Co19Cu5Zr11 catalyst with optimized reaction conditions to attain higher alcohol productivities of 1.1 gHA h-1 gcat-1 under stable operation for 150 h on stream, a 5-fold improvement over typically reported yields. Characterization reveals catalytic properties linked to superior activities despite moderate higher alcohol selectivities. To better reflect catalyst demands, we devise multi-objective optimization to maximize higher alcohol productivity while minimizing undesired CO2 and CH4 selectivities. An intrinsic trade-off between these metrics is uncovered, identifying Pareto-optimal catalysts not readily discernible by human experts. Finally, based on feature-importance analysis, we formulate data-informed guidelines to develop performance-specific FeCoCuZr systems. This approach goes beyond existing HAS catalyst design strategies, is adaptable to broader catalytic transformations, and fosters laboratory sustainability.

Adsorption and solar light activity of noble metal adatoms (Au and Zn) on Fe(111) surface: a first-principles study

Noble metals such as gold (Au), zinc (Zn), and iron (Fe) are highly significant in both fundamental and technological contexts owing to their applications in optoelectronics, optical coatings, transparent coatings, photodetectors, light-emitting devices, photovoltaics, nanotechnology, batteries, and thermal barrier coatings. This study presents a comprehensive investigation of the optoelectronic properties of Fe(111) and Au, Zn/Fe(111) materials using density functional theory (DFT) first-principles method with a focus on both materials' spin orientations. The optoelectronic properties were obtained employing the generalized gradient approximation (GGA) and the full-potential linearized augmented plane wave (FP-LAPW) approach, integrating the exchange-correlation function with the Hubbard potential U for improved accuracy. The arrangement of Fe(111) and Au, Zn/Fe(111) materials was found to lack an energy gap, indicating a metallic behavior in both the spin-up state and the spin-down state. The optical properties of Fe(111) and Au, Zn/Fe(111) materials, including their absorption coefficient, reflectivity, energy-loss function, refractive index, extinction coefficient, and optical conductivity, were thoroughly examined for both spin channels in the spectral region from 0.0 eV to 14 eV. The calculations revealed significant spin-dependent effects in the optical properties of the materials. Furthermore, this study explored the properties of the electronic bonding between several species in Fe(111) and Au, Zn/Fe(111) materials by examining the density distribution mapping of charge within the crystal symmetries.

Implementing the Blowers-Masel Approximation to Scale Activation Energy Based on Reaction Enthalpy in Mean-Field Microkinetic Modeling for Catalytic Methane Partial Oxidation

Mean-field microkinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a microkinetic model often need to be adjusted when changing species' binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and when fitting experimental data. When altering reaction enthalpies, the activation energies should also be reasonably altered to ensure realistic reaction rates. The Blowers-Masel approximation (BMA) relates the reaction barrier to the reaction enthalpy. Unlike the Brønsted-Evans-Polani relationship, the BMA requires less data because only one parameter, the intrinsic activation energy, needs to be determined. We validate this application of BMA relations to model surface reactions by comparing against density functional theory data taken from the literature. By incorporating the BMA rate description into the open-source Cantera software, we enable a new workflow, demonstrated herein, allowing rapid screening of catalysts using linear scaling relationships and BMA kinetics within the process simulation software. For demonstration purposes, a catalyst screening for catalytic methane partial oxidation on 81 hypothetical metals is conducted. We compared the results with and without BMA-corrected rates. The heat maps of various descriptors (e.g., CH4 conversion, syngas yield) show that using BMA rates instead of Arrhenius rates (with constant activation energies) changes which metals are most active. Heat maps of sensitivity analyses can help identify which reactions or species are the most influential in shaping the descriptor map patterns. Our findings indicate that while using BMA-adjusted rates did not markedly affect the most sensitive reactions, it did change the most influential species.

Machine Learning-Based Prediction of Activation Energies for Chemical Reactions on Metal Surfaces

In computational surface catalysis, the calculation of activation energies of chemical reactions is expensive, which, in many cases, limits our ability to understand complex reaction networks. Here, we present a universal, machine learning-based approach for the prediction of activation energies for reactions of C-, O-, and H-containing molecules on transition metal surfaces. We rely on generalized Bronsted-Evans-Polanyi relationships in combination with machine learning-based multiparameter regression techniques to train our model for reactions included in the University of Arizona Reaction database. In our best approach, we find a mean absolute error for activation energies within our test set of 0.14 eV if the reaction energy is known and 0.19 eV if the reaction energy is unknown. We expect that this methodology will often replace the explicit calculation of activation energies within surface catalysis when exploring large reaction networks or screening catalysts for desirable properties in the future.

Advanced zeolite and ordered mesoporous silica-based catalysts for the conversion of CO2 to chemicals and fuels

For many years, capturing, storing or sequestering CO₂ from concentrated emission sources or from air has been a powerful technique for reducing atmospheric CO₂. Moreover, the use of CO₂ as a C1 building block to mitigate CO₂ emissions and, at the same time, produce sustainable chemicals or fuels is a challenging and promising alternative to meet global demand for chemicals and energy. Hence, the chemical incorporation and conversion of CO₂ into valuable chemicals has received much attention in the last decade, since CO₂ is an abundant, inexpensive, nontoxic, nonflammable, and renewable one-carbon building block. Nevertheless, CO₂ is the most oxidized form of carbon, thermodynamically the most stable form and kinetically inert. Consequently, the chemical conversion of CO₂ requires highly reactive, rich-energy substrates, highly stable products to be formed or harder reaction conditions. The use of catalysts constitutes an important tool in the development of sustainable chemistry, since catalysts increase the rate of the reaction without modifying the overall standard Gibbs energy in the reaction. Therefore, special attention has been paid to catalysis, and in particular to heterogeneous catalysis because of its environmentally friendly and recyclable nature attributed to simple separation and recovery, as well as its applicability to continuous reactor operations. Focusing on heterogeneous catalysts, we decided to center on zeolite and ordered mesoporous materials due to their high thermal and chemical stability and versatility, which make them good candidates for the design and development of catalysts for CO₂ conversion. In the present review, we analyze the state of the art in the last 25 years and the potential opportunities for using zeolite and OMS (ordered mesoporous silica) based materials to convert CO₂ into valuable chemicals essential for our daily lives and fuels, and to pave the way towards reducing carbon footprint. In this review, we have compiled, to the best of our knowledge, the different reactions involving catalysts based on zeolites and OMS to convert CO₂ into cyclic and dialkyl carbonates, acyclic carbamates, 2-oxazolidones, carboxylic acids, methanol, dimethylether, methane, higher alcohols (C₂₊OH), C₂₊ (gasoline, olefins and aromatics), syngas (RWGS, dry reforming of methane and alcohols), olefins (oxidative dehydrogenation of alkanes) and simple fuels by photoreduction. The use of advanced zeolite and OMS-based materials, and the development of new processes and technologies should provide a new impulse to boost the conversion of CO₂ into chemicals and fuels.

Highly Efficient ZIF-67-Derived PtCo Alloy-CN Interface for Low-Temperature Aqueous-Phase Fischer-Tropsch Synthesis

Designing new materials for selective Fischer-Tropsch synthesis (FTS) is an elegant way to enhance local feedstock utilization like biomass and waste. In this approach, we have designed a thermally and chemically stable bimetallic PtCo/NC hybrid nanocomposite catalyst derived from a zeolitic imidazolate framework (ZIF-67, which contains cobalt as a metal center) through carbonization for low-temperature (413-473 K) aqueous-phase Fischer-Tropsch synthesis (AFTS). The selectivity of the desired range of hydrocarbons is adjusted using a highly dispersed PtCo bimetallic alloy, which facilitates extraordinary reduction of a metal oxide to active species by the synergic effect under the AFTS reaction conditions. The ZIF-derived catalyst tested in this study exhibited the highest activity to date for very low temperatures (433 K) in aqueous-phase Fischer-Tropsch synthesis with CO conversion rates between 0.61 and 1.20 mol_CO·mol_Co^-1·h^-1. Insights of the remarkable catalyst activity were examined by in situ X-ray photoelectron spectroscopy (XPS) studies corroborated by density functional theory (DFT) calculation. The bimetallic Co₃Pt (111) surface was found to be highly active for the C-C coupling reaction between surface-adsorbed C and CO, forming a CCO intermediate with a very low activation barrier (E_a = 0.37 eV), in comparison to the C-C coupling activation barrier obtained over the Co (111) surface (E_a = 0.87 eV). This unique approach and observations create a new path for developing next-generation advanced catalyst systems and processes for selective low-temperature FTS.

Constructing and interpreting volcano plots and activity maps to navigate homogeneous catalyst landscapes

Volcano plots and activity maps are powerful tools for studying homogeneous catalysis. Once constructed, they can be used to estimate and predict the performance of a catalyst from one or more descriptor variables. The relevance and utility of these tools has been demonstrated in several areas of catalysis, with recent applications to homogeneous catalysts having been pioneered by our research group. Both volcano plots and activity maps are built from linear free energy scaling relationships that connect the value of a descriptor variable(s) with the relative energies of other catalytic cycle intermediates/transition states. These relationships must be both constructed and postprocessed appropriately to obtain the resulting plots/maps; this process requires careful execution to obtain meaningful results. In this protocol, we provide a step-by-step guide to building volcano plots and activity maps using curated reaction profile data. The reaction profile data are obtained using density functional theory computations to model the catalytic cycle. In addition, we provide volcanic, a Python code that automates the steps of the process following data acquisition. Unlike the computation of individual reaction energy profiles, our tools lead to a holistic view of homogeneous catalyst performance that can be broadly applied for both explanatory and screening purposes.

Ensemble representation of catalytic interfaces: soloists, orchestras, and everything in-between

Catalytic systems are complex and dynamic, exploring vast chemical spaces on multiple timescales. In this perspective, we discuss the dynamic behavior of fluxional, heterogeneous thermal and electrocatalysts and the ensembles of many isomers which govern their behavior. We develop a new paradigm in catalysis theory in which highly fluxional systems, namely sub-nano clusters, isomerize on a much shorter timescale than that of the catalyzed reaction, so macroscopic properties arise from the thermal ensemble of isomers, not just the ground state. Accurate chemical predictions can only be reached through a many-structure picture of the catalyst, and we explain the breakdown of conventional methods such as linear scaling relations and size-selected prevention of sintering. We capitalize on the forward-looking discussion of the means of controlling the size of these dynamic ensembles. This control, such that the most effective or selective isomers can dominate the system, is essential for the fluxional catalyst to be practicable, and their targeted synthesis to be possible. It will also provide a fundamental lever of catalyst design. Finally, we discuss computational tools and experimental methods for probing ensembles and the role of specific isomers. We hope that catalyst optimization using chemically informed descriptors of ensemble nature and size will become a new norm in the field of catalysis and have broad impacts in sustainable energy, efficient chemical production, and more.

Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation

Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially relevant when considering complex materials spaces such as alloys, or complex reaction mechanisms with adsorbates that may exhibit bi- or higher-dentate adsorption motifs. Here we present a data-efficient approach to the prediction of binding motifs and associated adsorption enthalpies of complex adsorbates at transition metals and their alloys based on a customized Wasserstein Weisfeiler-Lehman graph kernel and Gaussian process regression. The model shows good predictive performance, not only for the elemental transition metals on which it was trained, but also for an alloy based on these transition metals. Furthermore, incorporation of minimal new training data allows for predicting an out-of-domain transition metal. We believe the model may be useful in active learning approaches, for which we present an ensemble uncertainty estimation approach.

Electrocatalysis in Alkaline Media and Alkaline Membrane-Based Energy Technologies

Hydrogen energy-based electrochemical energy conversion technologies offer the promise of enabling a transition of the global energy landscape from fossil fuels to renewable energy. Here, we present a comprehensive review of the fundamentals of electrocatalysis in alkaline media and applications in alkaline-based energy technologies, particularly alkaline fuel cells and water electrolyzers. Anion exchange (alkaline) membrane fuel cells (AEMFCs) enable the use of nonprecious electrocatalysts for the sluggish oxygen reduction reaction (ORR), relative to proton exchange membrane fuel cells (PEMFCs), which require Pt-based electrocatalysts. However, the hydrogen oxidation reaction (HOR) kinetics is significantly slower in alkaline media than in acidic media. Understanding these phenomena requires applying theoretical and experimental methods to unravel molecular-level thermodynamics and kinetics of hydrogen and oxygen electrocatalysis and, particularly, the proton-coupled electron transfer (PCET) process that takes place in a proton-deficient alkaline media. Extensive electrochemical and spectroscopic studies, on single-crystal Pt and metal oxides, have contributed to the development of activity descriptors, as well as the identification of the nature of active sites, and the rate-determining steps of the HOR and ORR. Among these, the structure and reactivity of interfacial water serve as key potential and pH-dependent kinetic factors that are helping elucidate the origins of the HOR and ORR activity differences in acids and bases. Additionally, deliberately modulating and controlling catalyst-support interactions have provided valuable insights for enhancing catalyst accessibility and durability during operation. The design and synthesis of highly conductive and durable alkaline membranes/ionomers have enabled AEMFCs to reach initial performance metrics equal to or higher than those of PEMFCs. We emphasize the importance of using membrane electrode assemblies (MEAs) to integrate the often separately pursued/optimized electrocatalyst/support and membranes/ionomer components. Operando/in situ methods, at multiscales, and ab initio simulations provide a mechanistic understanding of electron, ion, and mass transport at catalyst/ionomer/membrane interfaces and the necessary guidance to achieve fuel cell operation in air over thousands of hours. We hope that this Review will serve as a roadmap for advancing the scientific understanding of the fundamental factors governing electrochemical energy conversion in alkaline media with the ultimate goal of achieving ultralow Pt or precious-metal-free high-performance and durable alkaline fuel cells and related technologies.

A Multi-Process Model for Photocatalytic Reduction of CO2

Photocatalytic conversion of CO2 to fuel and valuable carbon compounds is a feasible method for large-scale reuse of CO2. However, it contains complex multi-processes and physical phenomena, limiting the performance...

Tracing the reactivity of single atom alloys for ethanol dehydrogenation using ab initio simulations

A full DFT parameterized MKM is used to accurately predict the reactivity trend for ethanol dehydrogenation reaction on SAAs.

Diffusion Barriers for Carbon Monoxide on the Cu(001) Surface Using Many-Body Perturbation Theory and Various Density Functionals

First-principles calculations play a key role in understanding the interactions of molecules with transition-metal surfaces and the energy profiles for catalytic reactions. However, many of the commonly used density functionals are not able to correctly predict the surface energy as well as the adsorption site preference for a key molecule such as CO, and it is not clear to what extent this shortcoming influences the prediction of reaction or diffusion pathways. Here, we report calculations of carbon monoxide diffusion on the Cu(001) surface along the [100] and [110] pathways, as well as the surface energy of Cu(001), and CO-adsorption energy and compare the performance of the Perdew-Burke-Ernzerhof (PBE), PBE + D2, PBE + D3, RPBE, Bayesian error estimation functional with van der Waals correlation (BEEF-vdW), HSE06 density functionals, and the random phase approximation (RPA), a post-Hartree-Fock method based on many-body perturbation theory. We critically evaluate the performance of these methods and find that RPA appears to be the only method giving correct site preference, overall barrier, adsorption enthalpy, and surface energy. For all of the other methods, at least one of these properties is not correctly captured. These results imply that many density functional theory (DFT)-based methods lead to qualitative and quantitative errors in describing CO interaction with transition-metal surfaces, which significantly impacts the description of diffusion pathways. It is well conceivable that similar effects exist when surface reactions of CO-related species are considered. We expect that the methodology presented here will be used to get more detailed insights into reaction pathways for CO conversion on transition-metal surfaces in general and Cu in particular, which will allow us to better understand the catalytic and electrocatalytic reactions involving CO-related species.

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)

Automatic mechanism generation is used to determine mechanisms for the CO2 hydrogenation on Ni(111) in a two-stage process while considering the correlated uncertainty in DFT-based energetic parameters systematically. In a coarse stage, all the possible chemistry is explored with gas-phase products down to the ppb level, while a refined stage discovers the core methanation submechanism. Five thousand unique mechanisms were generated, which contain minor perturbations in all parameters. Global uncertainty assessment, global sensitivity analysis, and degree of rate control analysis are performed to study the effect of this parametric uncertainty on the microkinetic model predictions. Comparison of the model predictions with experimental data on a Ni/SiO2 catalyst find a feasible set of microkinetic mechanisms within the correlated uncertainty space that are in quantitative agreement with the measured data, without relying on explicit parameter optimization. Global uncertainty and sensitivity analyses provide tools to determine the pathways and key factors that control the methanation activity within the parameter space. Together, these methods reveal that the degree of rate control approach can be misleading if parametric uncertainty is not considered. The procedure of considering uncertainties in the automated mechanism generation is not unique to CO2 methanation and can be easily extended to other challenging heterogeneously catalyzed reactions.

Moderate Surface Segregation Promotes Selective Ethanol Production in CO2 Hydrogenation Reaction over CoCu Catalysts

Cobalt-copper (CoCu) catalysts have industrial potential in CO/CO₂ hydrogenation reactions, and CoCu alloy has been elucidated as a major active phase during reactions. However, due to elemental surface segregation and dealloying phenomena, the actual surface morphology of CoCu alloy is still unclear. Combining theory and experiment, the dual effect of surface segregation and varied CO coverage over the CoCu(111) surface on the reactivity in CO₂ hydrogenation reactions is explored. The relationship between C-O bond scission and further hydrogenation of intermediate *CH₂ O was discovered to be a key step to promote ethanol production. The theoretical investigation suggests that moderate Co segregation provides a suitable surface Co ensemble with lateral interactions of co-adsorbed *CO, leading to promoted selectivity to ethanol, in agreement with theory-inspired experiments.

Toward alcohol synthesis from CO hydrogenation on Cu(111)-supported MoS2 - predictions from DFT+KMC

In the quest for cheap and efficient catalysts for alcohol synthesis from syngas, a material of interest is single-layer MoS₂ owing to its low cost, abundancy, and flexible structure. Because of the inertness of its basal plane, however, it is essential to find ways that make it catalytically active. Herein, by means of density functional theory based calculations of reaction pathways and activation energy barriers and accompanying kinetic Monte Carlo simulations, we show that while S vacancy row structures activate the MoS₂ basal plane, further enhancement of chemical activity and selectivity can be achieved by interfacing the MoS₂ layer with a metallic support. When defect-laden MoS₂ is grown on Cu(111), there is not only an increase in the active region (surface area of active sites) but also charge transfer from Cu to MoS₂, resulting in a shift of the Fermi level such that the frontier states (d orbitals of the exposed Mo atoms) appear close to it, making the MoS₂/Cu(111) system ready for catalytic activity. Our calculated thermodynamics of reaction pathways lead to the conclusion that the Cu(111) substrate promotes both methanol and ethanol as the products, while kinetic Monte Carlo simulations suggest a high selectivity toward the formation of ethanol.

Unravelling the reactivity of metastable molybdenum carbide nanoclusters in the C-H bond activation of methane, ethane and ethylene

C-H bond activation steps in non-oxidative methane dehydroaromatization (MDA), constitute a key functionalization of the reactant and adsorbed species to form aromatics. Previous studies have focused on studying the energetics of these steps at the most stable active sites involving molybdenum carbide species. Herein, a different paradigm is presented via studying the reactivity of a metastable molybdenum carbide (Mo₂C₆) nanocluster for the C-H bond activation of methane, ethane, and ethylene and comparing it with the reactivity of the lowest energy Mo₂C₆ nanocluster. Interestingly, the metastable nanocluster is observed to result in a consistent reduction (by half) in the C-H bond activation barrier of the respective alkane and alkene molecules compared to the global minimum isomer. This specific metastable form of the nanocluster is identified from a cascade genetic algorithm search, which facilitated a rigorous scan of the potential energy surface. We attribute this significant lowering of the C-H bond activation barrier to unique co-planar orbital overlap between the reactant molecule and active centers on the metastable nanocluster. Based on geometrical and orbital analysis of the transition states arising during the C-H bond activation of methane, ethane, and ethylene, a proton-coupled electron transfer mechanism is proposed that facilitated C-H bond cleavage. Motivated by the high reactivity for C-H bond activation observed on the metastable species, a contrasting framework to analyze the elementary-step rate contributions is presented. This is based on the statistical ensemble analysis of nanocluster isomers, where the calculated rates on respective isomers are normalized with respect to the Boltzmann probability distribution. From this framework, the metastable isomer is observed to provide significant contributions to the ensemble average representations of the rate constants calculated for C-H bond activation during the MDA reaction.

Microkinetic Modeling: A Tool for Rational Catalyst Design

The design of heterogeneous catalysts relies on understanding the fundamental surface kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help the researcher in streamlining the process of catalyst design. Microkinetic modeling is used to identify critical reaction intermediates and rate-determining elementary reactions, thereby providing vital information for designing an improved catalyst. In this review, we summarize general procedures for developing microkinetic models using reaction kinetics parameters obtained from experimental data, theoretical correlations, and quantum chemical calculations. We examine the methods required to ensure the thermodynamic consistency of the microkinetic model. We describe procedures required for parameter adjustments to account for the heterogeneity of the catalyst and the inherent errors in parameter estimation. We discuss the analysis of microkinetic models to determine the rate-determining reactions using the degree of rate control and reversibility of each elementary reaction. We introduce incorporation of Brønsted-Evans-Polanyi relations and scaling relations in microkinetic models and the effects of these relations on catalytic performance and formation of volcano curves are discussed. We review the analysis of reaction schemes in terms of the maximum rate of elementary reactions, and we outline a procedure to identify kinetically significant transition states and adsorbed intermediates. We explore the application of generalized rate expressions for the prediction of optimal binding energies of important surface intermediates and to estimate the extent of potential rate improvement. We also explore the application of microkinetic modeling in homogeneous catalysis, electro-catalysis, and transient reaction kinetics. We conclude by highlighting the challenges and opportunities in the application of microkinetic modeling for catalyst design.

CO2 reduction and ethane dehydrogenation on transition metal catalysts: mechanistic insights, reactivity trends and rational design of bimetallic alloys

Reactivity trends of transition metal catalysts, studied for the ethane dehydrogenation reaction using CO₂ as a mild oxidant.

Research progress of catalysts for synthesis of low-carbon alcohols from synthesis gas

In recent years, the application value of low-carbon alcohols (C1–C6 alcohols) in the fuel, chemical, environmental protection and other fields has become increasingly prominent. Catalytic conversion of synthesis gas to low-carbon alcohol is one of the important ways to realize the industrial production of low-carbon alcohol. Lack of high-performance catalysts is the main reason that restricts the industrial development of producing low-carbon alcohols from synthesis gas. The construction of a dual active-center catalyst with high activity and stability, and the study of its function and catalytic mechanism have become significantly important. In this paper, the characteristics of the reaction process of syngas to low-carbon alcohols, and the catalytic mechanism and preparation methods of different catalyst systems were reviewed, which provide the basis for further research on high performance catalysts.

The reaction process of the CO hydrogenation catalyzed synthesis of lower alcohols.

Mechanistic insights into the dominant reaction route and catalyst deactivation in biogas reforming using ab initio microkinetic modeling

Co and Ru are proposed as two possible catalyst candidates for the biogas reforming process as these convert most of the CH₄ to CO and yield less coke.

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. We finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

Opportunity of Atomically Thin Two-Dimensional Catalysts for Promoting CO2 Electroreduction

ConspectusExcessive use of fossil fuels has not only led to energy shortage but also caused serious environmental pollution problems due to the massive emissions of industrial waste gas. As the main component of industrial waste gas, CO₂ molecules can also be utilized as an important raw material for renewable fuels. Thus, the effective capture and conversion of CO₂ has been considered one of the best potential strategies to mitigate the energy crisis and lower the greenhouse effect simultaneously.In this case, CO₂ electroreduction to high-value-added chemicals provides an available approach to accomplish this important goal. Nonetheless, the CO₂ molecule is extremely stable with a high dissociation energy. With regard to the traditional electrocatalytic systems, there are three main factors that hinder their practical applications: (i) sluggish carrier transport dynamics; (ii) high energy barrier for CO₂ activation; (iii) poor product selectivity. Therefore, solving these three crucial problems is the key to the development of efficient electrocatalytic CO₂ reduction systems.Considering that the CO₂ molecule is a typical Lewis acid with a high first ionization energy and electronic affinity, electron-rich catalysts could help to activate the CO₂ molecule and improve the conversion efficiency. In view of this, atomically thin two-dimensional electrocatalysts, benefiting from their significantly increased density of states near the Fermi level, have great potential to effectively accelerate the dynamics of electron transport. Moreover, their high fraction of surface active sites and enhanced local charge density could remarkably reduce the energy barrier for CO₂ activation. Furthermore, their modulated electronic structure could alter the catalytic reaction pathway and improve the product selectivity. Meanwhile, the concise two-dimensional configuration facilitates in situ characterization as well as the establishment and simulation of theoretical models, which helps to reveal the mechanism of electrocatalytic CO₂ reduction, thereby speeding up the development of CO₂ conversion technology.In this Account, we summarize recent progress in tailoring the electronic structure of atomically thin two-dimensional electrocatalysts by different methods. Meanwhile, we highlight the structure-property relationship between the electronic structure regulation and the catalytic activity/product selectivity of atomically thin two-dimensional electrocatalysts, and discuss the underlying fundamental mechanism with the aid of in situ characterization techniques. Finally, we discuss the major challenges and opportunities for the future development of CO₂ electroreduction. It is expected that this Account will help researchers to better understand CO₂ electroreduction and guide better design of high-performance electrocatalytic systems.

Machine learning in chemical reaction space

Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for 'reactive' ML, we establish a first-principles database (Rad-6) containing closed and open-shell organic molecules, along with an associated database of chemical reaction energies (Rad-6-RE). We show that the special topology of reaction spaces, with central hub molecules involved in multiple reactions, requires a modification of existing compound space ML-concepts. Showcased by the application to methane combustion, we demonstrate that the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses.

Towards Experimental Handbooks in Catalysis

The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.